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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- esnli |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-base-e-snli-classification-nli-base |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: esnli |
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type: esnli |
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config: plain_text |
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split: validation |
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args: plain_text |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9108298866502319 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9109937004673847 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-base-e-snli-classification-nli-base |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the esnli dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2611 |
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- F1: 0.9108 |
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- Accuracy: 0.9110 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| |
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| 1.0317 | 0.05 | 400 | 0.5734 | 0.7771 | 0.7803 | |
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| 0.544 | 0.09 | 800 | 0.3994 | 0.8548 | 0.8555 | |
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| 0.4604 | 0.14 | 1200 | 0.3492 | 0.8681 | 0.8687 | |
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| 0.4235 | 0.19 | 1600 | 0.3323 | 0.8764 | 0.8777 | |
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| 0.3934 | 0.23 | 2000 | 0.3225 | 0.8831 | 0.8841 | |
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| 0.3863 | 0.28 | 2400 | 0.3086 | 0.8875 | 0.8872 | |
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| 0.3767 | 0.33 | 2800 | 0.2972 | 0.8892 | 0.8898 | |
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| 0.3726 | 0.37 | 3200 | 0.2910 | 0.8932 | 0.8936 | |
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| 0.3624 | 0.42 | 3600 | 0.2934 | 0.8934 | 0.8937 | |
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| 0.361 | 0.47 | 4000 | 0.2831 | 0.8989 | 0.8989 | |
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| 0.3553 | 0.51 | 4400 | 0.2905 | 0.8985 | 0.8993 | |
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| 0.3451 | 0.56 | 4800 | 0.2725 | 0.9019 | 0.9024 | |
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| 0.3475 | 0.61 | 5200 | 0.2712 | 0.9046 | 0.9051 | |
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| 0.3398 | 0.65 | 5600 | 0.2787 | 0.9024 | 0.9028 | |
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| 0.3322 | 0.7 | 6000 | 0.2697 | 0.9043 | 0.9046 | |
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| 0.3288 | 0.75 | 6400 | 0.2722 | 0.9006 | 0.9013 | |
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| 0.324 | 0.79 | 6800 | 0.2677 | 0.9066 | 0.9066 | |
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| 0.3335 | 0.84 | 7200 | 0.2629 | 0.9075 | 0.9077 | |
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| 0.3309 | 0.89 | 7600 | 0.2577 | 0.9058 | 0.9061 | |
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| 0.3236 | 0.93 | 8000 | 0.2561 | 0.9121 | 0.9121 | |
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| 0.3183 | 0.98 | 8400 | 0.2556 | 0.9084 | 0.9088 | |
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| 0.3022 | 1.03 | 8800 | 0.2668 | 0.9056 | 0.9064 | |
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| 0.2974 | 1.07 | 9200 | 0.2519 | 0.9087 | 0.9092 | |
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| 0.29 | 1.12 | 9600 | 0.2554 | 0.9103 | 0.9109 | |
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| 0.2855 | 1.16 | 10000 | 0.2611 | 0.9108 | 0.9110 | |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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